


'''simple docstring'''


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCAmelCase_						:  Tuple				  =						logging.get_logger(__name__)


UpperCAmelCase_						:  Tuple				  =						Dict[str, Any]
UpperCAmelCase_						:  str				  =						List[Prediction]





@add_end_docstrings(_snake_case   )
class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							def __init__(       self       ,   *__snake_case       ,   **__snake_case    ):
											super().__init__(*__snake_case       ,   **__snake_case    )

											if self.framework == "tf":
															raise ValueError(f"""The {self.__class__} is only available in PyTorch."""    )

											requires_backends(self       ,   """vision"""    )
											self.check_model_type(
											    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= {}
											if "threshold" in kwargs:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= kwargs["""threshold"""]
											return {}, {}, postprocess_kwargs






							def __call__(       self       ,   *__snake_case       ,   **__snake_case    ):
											return super().__call__(*__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= load_image(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= torch.IntTensor([[image.height, image.width]]    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.image_processor(images=[image]       ,   return_tensors="""pt"""    )
											if self.tokenizer is not None:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.tokenizer(text=inputs["""words"""]       ,   boxes=inputs["""boxes"""]       ,   return_tensors="""pt"""    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= target_size
											return inputs






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= model_inputs.pop("""target_size"""    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.model(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= outputs.__class__({"""target_size""": target_size, **outputs}    )
											if self.tokenizer is not None:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= model_inputs["""bbox"""]
											return model_outputs






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=0.9    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= model_outputs["""target_size"""]
											if self.tokenizer is not None:
															# This is a LayoutLMForTokenClassification variant.
															# The OCR got the boxes and the model classified the words.
															_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Optional[Any]							= target_size[0].tolist()

															def unnormalize(__snake_case    ):
																			return self._get_bounding_box(
																			    torch.Tensor(
																			        [
																			            (width * bbox[0] / 1000),
																			            (height * bbox[1] / 1000),
																			            (width * bbox[2] / 1000),
																			            (height * bbox[3] / 1000),
																			        ]    )    )

															_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= model_outputs["""logits"""].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
															_SCREAMING_SNAKE_CASE					:			Dict							= [unnormalize(__snake_case    ) for bbox in model_outputs["""bbox"""].squeeze(0    )]
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= ["""score""", """label""", """box"""]
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [dict(zip(__snake_case       ,   __snake_case    )    ) for vals in zip(scores.tolist()       ,   __snake_case       ,   __snake_case    ) if vals[0] > threshold]
											else:
															# This is a regular ForObjectDetectionModel
															_SCREAMING_SNAKE_CASE					:			List[str]							= self.image_processor.post_process_object_detection(__snake_case       ,   __snake_case       ,   __snake_case    )
															_SCREAMING_SNAKE_CASE					:			int							= raw_annotations[0]
															_SCREAMING_SNAKE_CASE					:			int							= raw_annotation["""scores"""]
															_SCREAMING_SNAKE_CASE					:			Tuple							= raw_annotation["""labels"""]
															_SCREAMING_SNAKE_CASE					:			Tuple							= raw_annotation["""boxes"""]

															_SCREAMING_SNAKE_CASE					:			str							= scores.tolist()
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [self.model.config.idalabel[label.item()] for label in labels]
															_SCREAMING_SNAKE_CASE					:			List[str]							= [self._get_bounding_box(__snake_case    ) for box in boxes]

															# {"scores": [...], ...} --> [{"score":x, ...}, ...]
															_SCREAMING_SNAKE_CASE					:			List[str]							= ["""score""", """label""", """box"""]
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
															    dict(zip(__snake_case       ,   __snake_case    )    )
															    for vals in zip(raw_annotation["""scores"""]       ,   raw_annotation["""labels"""]       ,   raw_annotation["""boxes"""]    )
															]

											return annotation






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											if self.framework != "pt":
															raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""    )
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= box.int().tolist()
											_SCREAMING_SNAKE_CASE					:			int							= {
											    """xmin""": xmin,
											    """ymin""": ymin,
											    """xmax""": xmax,
											    """ymax""": ymax,
											}
											return bbox








'''simple docstring'''


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


UpperCAmelCase_						:  List[Any]				  =						'sshleifer/bart-tiny-random'
UpperCAmelCase_						:  Tuple				  =						'patrickvonplaten/t5-tiny-random'





@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_      (       self    ):
											return AutoConfig.from_pretrained(__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.num_hidden_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			int							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			str							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=__snake_case    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   self.teacher_config.encoder_layers    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE						,			*_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=1       ,   d=1    )
											self.assertEqual(student.config.encoder_layers       ,   1    )
											self.assertEqual(student.config.decoder_layers       ,   1    )






							def UpperCAmelCase_      (       self    ):
											with self.assertRaises(__snake_case    ):
															create_student_by_copying_alternating_layers(__snake_case       ,   tempfile.mkdtemp()       ,   e=__snake_case       ,   d=__snake_case    )




